# matteocourthoud/awesome-causal-inference

A curated list of causal inference libraries, resources, and applications.

Repository: https://github.com/matteocourthoud/awesome-causal-inference
Canonical: https://ross.abutalabs.com/products/awesome-causal-inference
License: MIT
License Family: permissive
Topics: awesome, causal-inference
Last push: 2026-04-21T09:10:51+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 78, release rhythm 35, longevity 86
- inputs: {"age_days": 1217, "days_push": 134, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1187, forks 187 (observed 2026-08-28T04:03:55.212721+00:00)

## What it is
A curated awesome-list of causal inference resources including courses, books, libraries, papers, and industry applications. It serves as a reference directory rather than a software tool.

## Use cases
- find causal inference libraries
- learn causal inference from courses and books
- discover industry applications of causal inference
- find research papers on causal inference
- locate tutorials on causal methods

## When to choose
- you need a starting point to explore the causal inference ecosystem
- you want curated learning materials on causal methods
- you are comparing available causal inference libraries

## When to avoid
- you need runnable causal inference software itself
- you want a maintained code library rather than a link list

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: machine-learning, data-science, tutorials, awesome-lists
- platform: -
- tags: causal-inference, awesome-list, curated-resources, econometrics, statistics, web-server

## Member repositories
- matteocourthoud/awesome-causal-inference (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:55.212721+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:23:43.302909+00:00, confidence not recorded.
  - readme: https://github.com/matteocourthoud/awesome-causal-inference (fetched 2026-08-28T04:03:55.212721+00:00, sha 67c109cb308d)
- Data as of 2026-08-30T08:39:29.467469+00:00.